1. Field of the Invention
The present invention relates generally to the fields of microbial pathology and acquired resistance of pathogens and mathematical modeling. More specifically, the present invention provides a computer-implemented method to predict acquisition of resistance within a microbial population to an antimicrobial agent and a screening tool to predict efficacy of potential antimicrobial agents in preventing acquisition of resistance within microbial populations.
2. Description of the Related Art
Resistance to antimicrobial agents is a serious problem that renders the rapid development of new agents an urgent priority. The alarming spread of antimicrobial resistance is threatening the therapeutic armamentarium (1-11). It has been estimated that nearly 2 million people in the U.S. acquire bacterial infections while in the hospital and about 90,000 of them die every year. The total cost of antimicrobial resistance to U.S. society is nearly $5 billion annually. It is likely that effective treatment may not be available for many common infections in the not too distant future and the risk of going back to the pre-antibiotic era in the event of an outbreak is present (4). Broad-spectrum antimicrobial resistance in HIV, tuberculosis, Gram-negative bacteria, e.g., Pseudomaonas aeruginosa, Acinetobacter baumannii, etc., and agents implicated in bioterrorism, e.g., Bacillus anthracis, is especially worrisome and has world-wide implications. It is therefore imperative that new and effective antimicrobial agents are developed rapidly to keep up with our combat against infections caused by these pathogens.
As widely appreciated as the magnitude of this problem may be, the traditional approach to the development of antimicrobial agents is unlikely to meet this critical need. The traditional approach has focused on the identification of new metabolic targets and agents to interfere with essential pathways. Relatively little attention has been paid to the impact of the dosing regimen, i.e., dose and dosing frequency, of an active agent on the emergence of resistance.
In-vitro and in-vivo experimental data demonstrate that dosing regimen may play an important role in the development of resistance; sub-optimal dosing regimens represent a selective pressure that facilitates resistance development, whereas using optimal dosing regimens may suppress the emergence of resistance (2-3, 12). However, multiple modifiable factors, e.g., the total daily dose, dosing frequency, length of (intravenous) administration and duration of therapy, etc., are involved in rational design of dosing regimens. Each factor may have a significant impact on the killing activity and propensity to suppress resistance emergence, depending on the pharmacodynamic properties of the agents and clinically achievable concentrations associated with acceptable toxicity.
The numerous combinations of these variables involved in designing dosing regimens are prohibitory for comprehensive laboratory or clinical evaluation of all the different scenarios. In view of the labor-intensiveness of each investigation, several regimens are often empirically chosen to be studied. This approach is poorly guided and may lead to prematurely abandoning the development of good agent candidates. As a result, the potentials of new agents may not be thoroughly realized.
Pharmacodynamic modeling has been used as a decision support tool to facilitate rational dosage design. It emphasizes the fact that effective antimicrobial treatment is attributed to neither antimicrobial agent potency (exposure) nor pathogen susceptibility alone, but rather a complex interplay of both factors. In spite of that, conventional modeling methods may be overly simplistic, relying on surrogate pharmacodynamic indices, e.g., area under the concentration-time profile (AUC)/minimum inhibitory concentration (MIC), percentage of dosing interval during which concentration is above MIC (% T>MIC), etc., to characterize outcomes. Conventional modeling methods typically take a snapshot of microbial burden at the end of an observation period and curve-fit the observations as a function of the surrogate index without making use of information at intermediate stages of the observation period (6-9, 13-16). Not surprisingly, these modeling approaches have restricted predictive ability and their limitations have been reviewed previously (17). On the other hand, modeling methods that make use of all available information on microbial burden during an observation period offer distinct advantages, in terms of being capable of accounting for the selective pressure that an antimicrobial agent exerts on a microbial population and to make useful predictions of microbial response to antimicrobial agents (18).
The ability to predict microbial response to antimicrobial agents is of great importance in the efforts in combating antimicrobial resistance. If the most effective dosing regimen of an antimicrobial agent can be identified and used clinically, it is hoped that the emergence of antimicrobial resistance can be suppressed (or delayed). Mathematical modeling and computer simulation of microbial response to antimicrobial agents hold great promise in accelerating and improving the development of antimicrobial agents. They have the capability to perform comprehensive screening of a large number of agent candidates to guide highly targeted testing. Thus, only promising agents and dosing regimens with high probabilities of success need be investigated subsequently in (pre-) clinical studies.
Thus, there is a significant need in the art for improvements in the area of high throughput screening for antimicrobial dosing regimens to increase the efficiency and cost-effectiveness of drug development. Specifically, the prior art is deficient in systems and methods of computer-implemented mathematical modeling useful to predict microbial response to a large number of antimicrobial agent and to design dosing regimens therefrom. The present invention fulfills this long-standing need and desire in the art.